Predicting incomplete gene microarray data with the use of supervised learning algorithms
- 1 October 2010
- journal article
- Published by Elsevier BV in Pattern Recognition Letters
- Vol. 31 (13), 2061-2069
- https://doi.org/10.1016/j.patrec.2010.05.006
Abstract
No abstract availableThis publication has 31 references indexed in Scilit:
- K nearest neighbours with mutual information for simultaneous classification and missing data imputationNeurocomputing, 2009
- Impact of imputation of missing values on classification error for discrete dataPattern Recognition, 2008
- Robust data imputationComputational Biology and Chemistry, 2008
- Improving cluster-based missing value estimation of DNA microarray dataBiomolecular Engineering, 2007
- Microarray missing data imputation based on a set theoretic framework and biological knowledgeNucleic Acids Research, 2006
- LSimpute: accurate estimation of missing values in microarray data with least squares methodsNucleic Acids Research, 2004
- Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclassesProceedings of the National Academy of Sciences of the United States of America, 2001
- Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression MonitoringScience, 1999
- Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arraysProceedings of the National Academy of Sciences of the United States of America, 1999
- THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMSAnnals of Eugenics, 1936